Comparison of Multiple Linear Regression and Biotic Ligand Models for Predicting Acute and Chronic Zinc Toxicity to Freshwater Organisms

Multiple linear regression (MLR) models for predicting zinc (Zn) toxicity to freshwater organisms were developed based on three toxicity-modifying factors: dissolved organic carbon (DOC), hardness, and pH. Species-specific, stepwise MLR models were developed to predict acute Zn toxicity to four invertebrates and two fish, and chronic toxicity to three invertebrates, a fish, and a green alga. Stepwise regression analyses found that hardness had the most consistent influence on Zn toxicity among species, whereas DOC and pH had a variable influence. Pooled acute and chronic MLR models were also developed, and a k-fold cross-validation was used to evaluate the fit and predictive ability of the pooled MLR models. The pooled MLR models and an updated Zn biotic ligand model (BLM) performed similarly based on (1) R² , (2) the percentage of effect concentration (ECx) predictions within a factor of 2.0 of observed ECx, and (3) residuals of observed/predicted ECx versus observed ECx, DOC, hardness, and pH. Although fit of the pooled models to species-specific toxicity data differed among species, species-specific differences were consistent between the BLM and MLR models. Consistency in the performance of the two models across species indicates that additional terms, beyond DOC, hardness, and pH, included in the BLM do not help explain the differences among species. The pooled acute and chronic MLR models and BLM both performed better than the US Environmental Protection Agency's existing hardness-based model. We therefore conclude that both MLR models and the BLM provide an improvement over the existing hardness-only models and that either could be used for deriving ambient water quality criteria. Environ Toxicol Chem 2023;42:393-413. © 2022 SETAC.

Effects of Maternally Transferred Egg Selenium on Embryo-Larval Survival, Growth, and Development in Arctic Grayling (Thymallus arcticus)

Selenium (Se) toxicity to fish is primarily manifested via maternal transfer to the eggs, which may result in adverse effects on larval survival and development. The present study assessed the effects of egg Se concentrations derived via maternal transfer on early life-stage development, survival, and growth of Arctic grayling (Thymallus arcticus), a salmonid species not previously assessed for Se sensitivity. Fish gametes were collected from 4 streams in Alaska known to exhibit a range of egg Se concentrations. Eggs were fertilized and reared in the laboratory from hatch through post-swim-up. Larvae were assessed for survival, length, and weight, as well as deformities (skeletal, craniofacial, fin-fold) and edema based on a graduated severity index. Eggs from a total of 47 females were collected, with egg Se concentrations ranging from 3.3 to 33.9 mg kg^-1  dry weight. No relationships were observed between larval endpoints evaluated and parent females' egg, muscle, or whole-body Se concentrations. Therefore, Se 10% effective concentrations (EC10s) were defined as the maximum measured Se concentrations: >33.9, >17.6, and >19.7 mg kg^-1  dry weight for eggs, muscle, and whole-body tissue, respectively. Collectively, these data indicate that Arctic grayling are relatively insensitive to maternally transferred Se compared to other fish species. Environ Toxicol Chem 2021;40:380-389. © 2020 SETAC.

Updated Multiple Linear Regression Models for Predicting Chronic Aluminum Toxicity to Freshwater Aquatic Organisms and Developing Water Quality Guidelines

Multiple linear regression (MLR) models for predicting chronic aluminum toxicity to a cladoceran (Ceriodaphnia dubia) and a fish (Pimephales promelas) as a function of 3 toxicity-modifying factors (TMFs)-dissolved organic carbon (DOC), pH, and hardness-have been published previously. However, the range over which data for these TMFs were available was somewhat limited. To address this limitation, additional chronic toxicity tests with these species were subsequently conducted to expand the DOC range up to 12 mg/L, the pH range up to 8.7, and the hardness range up to 428 mg/L. The additional toxicity data were used to update the chronic MLR models. The adjusted R² for the C. dubia 20% effect concentration (EC20) model increased from 0.71 to 0.92 with the additional toxicity data, and the predicted R² increased from 0.57 to 0.89. For P. promelas, the adjusted R² increased from 0.87 to 0.92 and the predicted R² increased from 0.72 to 0.87. The high predicted R² relative to the adjusted R² indicates that the models for both species are not overly parameterized. When data for C. dubia and P. promelas were pooled, the adjusted R² values were comparable to the species-specific models (0.90 and 0.88 for C. dubia and P. promelas, respectively). This indicates that chronic aluminum EC20s for C. dubia and P. promelas respond similarly to variation in DOC, pH, and hardness. Overall, the pooled model predicted EC20s that were within a factor of 2 of observed in 100% of the C. dubia tests and 94% of the P. promelas tests. Environ Toxicol Chem 2020;39:1724-1736. © 2020 SETAC.

Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines

The bioavailability of aluminum (Al) to freshwater aquatic organisms varies as a function of several water chemistry parameters, including pH, dissolved organic carbon (DOC), and water hardness. We evaluated the ability of multiple linear regression (MLR) models to predict chronic Al toxicity to a green alga (Pseudokirchneriella subcapitata), a cladoceran (Ceriodaphnia dubia), and a fish (Pimephales promelas) as a function of varying DOC, pH, and hardness conditions. The MLR models predicted toxicity values that were within a factor of 2 of observed values in 100% of the cases for P. subcapitata (10 and 20% effective concentrations [EC10s and EC20s]), 91% of the cases for C. dubia (EC10s and EC20s), and 95% (EC10s) and 91% (EC20s) of the cases for P. promelas. The MLR models were then applied to all species with Al toxicity data to derive species and genus sensitivity distributions that could be adjusted as a function of varying DOC, pH, and hardness conditions (the P. subcapitata model was applied to algae and macrophytes, the C. dubia model was applied to invertebrates, and the P. promelas model was applied to fish). Hazardous concentrations to 5% of the species or genera were then derived in 2 ways: 1) fitting a log-normal distribution to species-mean EC10s for all species (following the European Union methodology), and 2) fitting a triangular distribution to genus-mean EC20s for animals only (following the US Environmental Protection Agency methodology). Overall, MLR-based models provide a viable approach for deriving Al water quality guidelines that vary as a function of DOC, pH, and hardness conditions and are a significant improvement over bioavailability corrections based on single parameters. Environ Toxicol Chem 2018;37:80-90. © 2017 SETAC.

Lentic, lotic, and sulfate-dependent waterborne selenium screening guidelines for freshwater systems

There is consensus that fish are the most sensitive aquatic organisms to selenium (Se) and that Se concentrations in fish tissue are the most reliable indicators of potential toxicity. Differences in Se speciation, biological productivity, Se concentration, and parameters that affect Se bioavailability (e.g., sulfate) may influence the relationship between Se concentrations in water and fish tissue. It is desirable to identify environmentally protective waterborne Se guidelines that, if not exceeded, reduce the need to directly measure Se concentrations in fish tissue. Three factors that should currently be considered in developing waterborne Se screening guidelines are 1) differences between lotic and lentic sites, 2) the influence of exposure concentration on Se partitioning among compartments, and 3) the influence of sulfate on selenate bioavailability. Colocated data sets of Se concentrations in 1) water and particulates, 2) particulates and invertebrates, and 3) invertebrates and fish tissue were compiled; and a quantile regression approach was used to derive waterborne Se screening guidelines. Use of a regression-based approach for describing relationships in Se concentrations between compartments reduces uncertainty associated with selection of partitioning factors that are generally not constant over ranges of exposure concentrations. Waterborne Se screening guidelines of 6.5 and 3.0 μg/L for lotic and lentic water bodies were derived, and a sulfate-based waterborne Se guideline equation for selenate-dominated lotic waters was also developed. Environ Toxicol Chem 2017;36:2503-2513. © 2017 SETAC.

Long-term monitoring of arsenic, copper, selenium, and other elements in Great Salt Lake (Utah, USA) surface water, brine shrimp, and brine flies

This paper presents long-term monitoring data for 19 elements with a focus on arsenic (As), copper (Cu), and selenium (Se), in surface water (2002-2011), brine shrimp (2001-2011), and brine flies (1995-1996) collected from Great Salt Lake (GSL, Utah, USA). In open surface waters, mean (±standard deviation [SD]; range; n) As concentrations were 112 (±22.1; 54.0-169; 47) and 112 μg/L (±35.6; 5.1-175; 68) in filtered and unfiltered surface water samples, respectively, and 16.3 μg/g (±5.6; 5.1-35.2; 62) dry weight (dw) in brine shrimp. Mean (±SD; range; n) Cu concentrations were 4.2 (±2.1; 1.3-12.5; 47) and 6.9 μg/L (±6.6; 1.9-38.1; 68) in filtered and unfiltered surface water samples, respectively, and 20.6 μg/g (±18.4; 5.4-126; 62) dw in brine shrimp. Finally, mean (±SD; range; n) dissolved and total recoverable Se concentrations were 0.6 (±0.1; 0.4-1.2; 61) and 0.9 μg/L (±0.7; 0.5-3.6; 89), respectively, and 3.6 μg/g (±2.2; 1.1-14.9; 98) dw in brine shrimp. Thus, Se in open lake surface waters was most often in the range of 0.5-1 μg/L, and concentrations in both surface water and brine shrimp were comparable to concentrations measured in other monitoring programs for the GSL. Temporally, the statistical significance of differences in mean dissolved or total recoverable As, Cu, and Se concentrations between years was highly variable depending which test statistic was used, and there was no clear evidence of increasing or decreasing trends. In brine shrimp, significant differences in annual mean concentrations of As, Cu, and Se were observed using both parametric and nonparametric statistical approaches, but, as for water, there did not appear to be a consistent increase or decrease in concentrations of these elements over time.

Setting site-specific water-quality standards by using tissue residue thresholds and bioaccumulation data. Part 2. Calculating site-specific selenium water-quality standards for protecting fish and birds

In a companion paper, a method for deriving tissue residue-based site-specific water-quality standards (SSWQSs) was described. In this paper, the methodology is applied to selenium (Se) as an example. Models were developed to describe Se bioaccumulation in aquatic-dependent bird eggs and whole fish. A simple log-linear model best described Se accumulation in bird eggs (r2 = 0.50). For fish, separate hockey stick regressions were developed for lentic (r2 = 0.65) and lotic environments (r2 = 0.37). The low r2 value for the lotic fish model precludes its reliable use at this time. Corresponding tissue residue criteria (i.e., tissue thresholds) for bird eggs and whole fish also were identified and example model predictions were made. The models were able to predict SSWQSs over a wide range of water-tissue combinations that might be encountered in the environment. The models also were shown to be sensitive to variability in measured tissue residues with relatively small changes in variability (as characterized by the standard error) resulting in relatively large differences in SSWQSs.

Setting site-specific water-quality standards by using tissue residue criteria and bioaccumulation data. Part 1. Methodology

We have developed a method for determining site-specific water-quality standards (SSWQSs) for substances regulated based on tissue residues. The method uses a multisite regression model to solve for the conditional prior probability density function (PDF) on water concentration, given that tissue concentration equals a tissue residue threshold. The method then uses site-specific water and tissue concentration data to update the probabilities on a Monte Carlo sample of the prior PDF by using Bayesian Monte Carlo analysis. The resultant posterior PDF identifies the water concentration that, if met at the site, would provide a desired level of confidence of meeting the tissue residue threshold contingent on model assumptions. This allows for derivation of a SSWQS. The method is fully reproducible, statistically rigorous, and easily implemented. A useful property of the method is that the model is sensitive to the amount of site-specific data available, that is, a more conservative or protective number (water concentration) is derived when the data set is small or the variance is large. Likewise, as the site water concentration increases above the water-quality standard, more site-specific information is needed to demonstrate a safe concentration at the site. A companion paper demonstrates the method by using selenium as an example.

Analysis of field and laboratory data to derive selenium toxicity thresholds for birds

In this paper, we critically evaluate the statistical approaches and datasets previously used to derive chronic egg selenium thresholds for mallard ducks (laboratory data) and black-necked stilts (field data). These effect concentration thresholds of 3%, 10% (EC10), or 20% have been used by regulatory agencies to set avian protection criteria and site remediation goals, thus the need for careful assessment of the data. The present review indicates that the stilt field dataset used to establish a frequently cited chronic avian egg selenium threshold of 6 mg/kg dry weight lacks statistical robustness (r2 = 0.19-0.28 based on generalized linear models), suggesting that stilt embryo sensitivity to selenium is highly variable or that factors other than selenium are principally responsible for the increase in effects observed at the lower range of this dataset. Hockey stick regressions used with the stilt field dataset improve the statistical relationship (r2 = 0.90-0.97) but result in considerably higher egg selenium thresholds (EC10 = 21-31 mg/kg dry wt). Laboratory-derived (for mallards) and field-derived (for stilts) teratogenicity EC10 values are quite similar (16-24 mg/kg dry wt). Laboratory data regarding mallard egg inviability and duckling mortality data provide the most sensitive and statistically robust chronic threshold (EC10) with logit, probit, and hockey stick regressions fitted to laboratory data, resulting in mean egg selenium EC10 values of 12 to 15 mg/kg dry weight (r2 = 0.75-0.90).